Formula 1 is the highest class of international racing for open-wheel single-seater formula racing cars. The FIA Formula 1 World Championship has been one of the premier forms of racing around the world since 1950.

The word formula refers to the set of rules to which all participants’ cars must conform and the season consists of a series of races, known as Grands Prix (GP), in multiple countries and continents around the world.

1. Problem and Objective
2. Data preparation
3. Different type of Regressions
4. Finding and conclusion

Is it possible to predict the number of total points each driver will finish the Formula 1 Championship with?

The Ergast Developer API is an experimental web service which provides a historical record of motor racing data for non-commercial purposes. The API provides data for the Formula One series, from the beginning of the world championships in 1950 to today. It’s even possible to download the database tables in CSV format or the SQL image.

Each CSV file contains a single database table and the first line of each file contains the column headers. The tables are described in the User Guide.

Results

Drivers

Constructors

Races and circuits

• winRate: Likelihood of winning a race for every driver

• qualiRate: Likelihood of qualifying first for every driver

Let’s find out:

• the number of points awarded to the 1st place over the years
• the number of races on the calendar over the years
• the current point system for every race

and 1 point is added to the driver who made the fastest lap during the race.

• new points using the last point system introduced by the FIA
• average points scored by each driver
• copy the qualifying and win rates to the training dataframe

Linear regression is a linear approach for modelling the relationship between a scalar response and one or more explanatory variables where the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data.

with just half the points

with more features

Polynomial regression is a form of regression analysis where the relationship between the independent variable and the dependent variable is modeled as an ^nth degree polynomial in x. It fits a nonlinear relationship between the value of x and the corresponding conditional mean of y.

with one feature

with more features

With the Bayesian regression, we formulate linear regression using probability distributions rather than point estimates. The response, is not estimated as a single value, but is assumed to be drawn from a probability distribution.

with one feauture

with more features

Comparison between the R-squared of all the linear and polynomial regressions and using the variance for the bayesan linear regressions divided by original and new points systems.

Using the best suited model, the Linear regression model with more features in this case, and assuming that we are in the second half of the championship we can predict the points in the end will be these: